7 citations · 14 across the 5 of their papers we have counts for
5 papers
4D Contrastive Superflows are Dense 3D Representation Learners
Xiang Xu, Lingdong Kong, Hui Shuai +5
In the realm of autonomous driving, accurate 3D perception is the foundation. However, developing such models relies on extensive human annotations -- a process that is both costly…
RoboDepth: Robust Out-of-Distribution Depth Estimation under Corruptions
Lingdong Kong, Shaoyuan Xie, Hanjiang Hu +3
Depth estimation from monocular images is pivotal for real-world visual perception systems. While current learning-based depth estimation models train and test on meticulously cura…
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Youquan Liu, Runnan Chen, Xin Li +9
Point-, voxel-, and range-views are three representative forms of point clouds. All of them have accurate 3D measurements but lack color and texture information. RGB images are a n…
SAD: Segment Any RGBD
Jun Cen, Yizheng Wu, Kewei Wang +6
The Segment Anything Model (SAM) has demonstrated its effectiveness in segmenting any part of 2D RGB images. However, SAM exhibits a stronger emphasis on texture information while…
RoboBEV: Towards Robust Bird's Eye View Perception under Corruptions
Shaoyuan Xie, Lingdong Kong, Wenwei Zhang +4
The recent advances in camera-based bird's eye view (BEV) representation exhibit great potential for in-vehicle 3D perception. Despite the substantial progress achieved on standard…